
# import all packages and set plots to be embedded inline
import numpy as np
import pandas as pd

import matplotlib.pyplot as plt
import seaborn as sns


from pandas_profiling import ProfileReport


pd.set_option('display.max_colwidth', None) # to display the whole strings and not collapse them
pd.set_option('display.max_columns', None) # to not collapse columns when viewing data
pd.set_option('display.max_rows', None) # to display all rows in the output

import warnings
warnings.filterwarnings("ignore") # warnings off

%matplotlib inline


prosper_df = pd.read_csv('prosperLoanData.csv')
prosper_df


prosper_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 113937 entries, 0 to 113936
Data columns (total 81 columns):
 #   Column                               Non-Null Count   Dtype  
---  ------                               --------------   -----  
 0   ListingKey                           113937 non-null  object 
 1   ListingNumber                        113937 non-null  int64  
 2   ListingCreationDate                  113937 non-null  object 
 3   CreditGrade                          28953 non-null   object 
 4   Term                                 113937 non-null  int64  
 5   LoanStatus                           113937 non-null  object 
 6   ClosedDate                           55089 non-null   object 
 7   BorrowerAPR                          113912 non-null  float64
 8   BorrowerRate                         113937 non-null  float64
 9   LenderYield                          113937 non-null  float64
 10  EstimatedEffectiveYield              84853 non-null   float64
 11  EstimatedLoss                        84853 non-null   float64
 12  EstimatedReturn                      84853 non-null   float64
 13  ProsperRating (numeric)              84853 non-null   float64
 14  ProsperRating (Alpha)                84853 non-null   object 
 15  ProsperScore                         84853 non-null   float64
 16  ListingCategory (numeric)            113937 non-null  int64  
 17  BorrowerState                        108422 non-null  object 
 18  Occupation                           110349 non-null  object 
 19  EmploymentStatus                     111682 non-null  object 
 20  EmploymentStatusDuration             106312 non-null  float64
 21  IsBorrowerHomeowner                  113937 non-null  bool   
 22  CurrentlyInGroup                     113937 non-null  bool   
 23  GroupKey                             13341 non-null   object 
 24  DateCreditPulled                     113937 non-null  object 
 25  CreditScoreRangeLower                113346 non-null  float64
 26  CreditScoreRangeUpper                113346 non-null  float64
 27  FirstRecordedCreditLine              113240 non-null  object 
 28  CurrentCreditLines                   106333 non-null  float64
 29  OpenCreditLines                      106333 non-null  float64
 30  TotalCreditLinespast7years           113240 non-null  float64
 31  OpenRevolvingAccounts                113937 non-null  int64  
 32  OpenRevolvingMonthlyPayment          113937 non-null  float64
 33  InquiriesLast6Months                 113240 non-null  float64
 34  TotalInquiries                       112778 non-null  float64
 35  CurrentDelinquencies                 113240 non-null  float64
 36  AmountDelinquent                     106315 non-null  float64
 37  DelinquenciesLast7Years              112947 non-null  float64
 38  PublicRecordsLast10Years             113240 non-null  float64
 39  PublicRecordsLast12Months            106333 non-null  float64
 40  RevolvingCreditBalance               106333 non-null  float64
 41  BankcardUtilization                  106333 non-null  float64
 42  AvailableBankcardCredit              106393 non-null  float64
 43  TotalTrades                          106393 non-null  float64
 44  TradesNeverDelinquent (percentage)   106393 non-null  float64
 45  TradesOpenedLast6Months              106393 non-null  float64
 46  DebtToIncomeRatio                    105383 non-null  float64
 47  IncomeRange                          113937 non-null  object 
 48  IncomeVerifiable                     113937 non-null  bool   
 49  StatedMonthlyIncome                  113937 non-null  float64
 50  LoanKey                              113937 non-null  object 
 51  TotalProsperLoans                    22085 non-null   float64
 52  TotalProsperPaymentsBilled           22085 non-null   float64
 53  OnTimeProsperPayments                22085 non-null   float64
 54  ProsperPaymentsLessThanOneMonthLate  22085 non-null   float64
 55  ProsperPaymentsOneMonthPlusLate      22085 non-null   float64
 56  ProsperPrincipalBorrowed             22085 non-null   float64
 57  ProsperPrincipalOutstanding          22085 non-null   float64
 58  ScorexChangeAtTimeOfListing          18928 non-null   float64
 59  LoanCurrentDaysDelinquent            113937 non-null  int64  
 60  LoanFirstDefaultedCycleNumber        16952 non-null   float64
 61  LoanMonthsSinceOrigination           113937 non-null  int64  
 62  LoanNumber                           113937 non-null  int64  
 63  LoanOriginalAmount                   113937 non-null  int64  
 64  LoanOriginationDate                  113937 non-null  object 
 65  LoanOriginationQuarter               113937 non-null  object 
 66  MemberKey                            113937 non-null  object 
 67  MonthlyLoanPayment                   113937 non-null  float64
 68  LP_CustomerPayments                  113937 non-null  float64
 69  LP_CustomerPrincipalPayments         113937 non-null  float64
 70  LP_InterestandFees                   113937 non-null  float64
 71  LP_ServiceFees                       113937 non-null  float64
 72  LP_CollectionFees                    113937 non-null  float64
 73  LP_GrossPrincipalLoss                113937 non-null  float64
 74  LP_NetPrincipalLoss                  113937 non-null  float64
 75  LP_NonPrincipalRecoverypayments      113937 non-null  float64
 76  PercentFunded                        113937 non-null  float64
 77  Recommendations                      113937 non-null  int64  
 78  InvestmentFromFriendsCount           113937 non-null  int64  
 79  InvestmentFromFriendsAmount          113937 non-null  float64
 80  Investors                            113937 non-null  int64  
dtypes: bool(3), float64(50), int64(11), object(17)
memory usage: 68.1+ MB


prosper_df.describe(include='all')


df_description = pd.read_csv('Prosper Loan Data - Variable Definitions - Sheet1.csv')


#get description of all our columns in dataset

#source : https://stackoverflow.com/questions/33181846/programmatically-convert-pandas-dataframe-to-markdown-table
def pandas_df_to_markdown_table(df):
    from IPython.display import Markdown, display
    fmt = ['---' for i in range(len(df.columns))]
    df_fmt = pd.DataFrame([fmt], columns=df.columns)
    df_formatted = pd.concat([df_fmt, df])
    display(Markdown(df_formatted.to_csv(sep="|", index=False)))

pandas_df_to_markdown_table(df_description)


print(f'The number of entries are {len(prosper_df)} and no. of unique listing are {prosper_df.ListingKey.nunique()}')

The number of entries are 113937 and no. of unique listing are 113066


prosper_df[prosper_df.ListingKey.duplicated() == True].count()['ListingKey']

871


prosper_df[prosper_df.ListingKey.duplicated() == True]['ListingKey'].head()

9       0F043596202561788EA13D5
999     0A0635972629771021E38F3
2539    2D2635984503681189056B4
4942    4B7E3590031274113F1FD34
5812    94B035909835592957503E6
Name: ListingKey, dtype: object


prosper_df[prosper_df.ListingKey == '0A0635972629771021E38F3']


prosper_df[prosper_df.ListingKey == '2D2635984503681189056B4']


prosper_df.drop_duplicates(subset = 'ListingKey', inplace = True)


prosper_df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 113066 entries, 0 to 113936
Data columns (total 81 columns):
 #   Column                               Non-Null Count   Dtype  
---  ------                               --------------   -----  
 0   ListingKey                           113066 non-null  object 
 1   ListingNumber                        113066 non-null  int64  
 2   ListingCreationDate                  113066 non-null  object 
 3   CreditGrade                          28953 non-null   object 
 4   Term                                 113066 non-null  int64  
 5   LoanStatus                           113066 non-null  object 
 6   ClosedDate                           55076 non-null   object 
 7   BorrowerAPR                          113041 non-null  float64
 8   BorrowerRate                         113066 non-null  float64
 9   LenderYield                          113066 non-null  float64
 10  EstimatedEffectiveYield              83982 non-null   float64
 11  EstimatedLoss                        83982 non-null   float64
 12  EstimatedReturn                      83982 non-null   float64
 13  ProsperRating (numeric)              83982 non-null   float64
 14  ProsperRating (Alpha)                83982 non-null   object 
 15  ProsperScore                         83982 non-null   float64
 16  ListingCategory (numeric)            113066 non-null  int64  
 17  BorrowerState                        107551 non-null  object 
 18  Occupation                           109537 non-null  object 
 19  EmploymentStatus                     110811 non-null  object 
 20  EmploymentStatusDuration             105441 non-null  float64
 21  IsBorrowerHomeowner                  113066 non-null  bool   
 22  CurrentlyInGroup                     113066 non-null  bool   
 23  GroupKey                             13339 non-null   object 
 24  DateCreditPulled                     113066 non-null  object 
 25  CreditScoreRangeLower                112475 non-null  float64
 26  CreditScoreRangeUpper                112475 non-null  float64
 27  FirstRecordedCreditLine              112369 non-null  object 
 28  CurrentCreditLines                   105462 non-null  float64
 29  OpenCreditLines                      105462 non-null  float64
 30  TotalCreditLinespast7years           112369 non-null  float64
 31  OpenRevolvingAccounts                113066 non-null  int64  
 32  OpenRevolvingMonthlyPayment          113066 non-null  float64
 33  InquiriesLast6Months                 112369 non-null  float64
 34  TotalInquiries                       111907 non-null  float64
 35  CurrentDelinquencies                 112369 non-null  float64
 36  AmountDelinquent                     105444 non-null  float64
 37  DelinquenciesLast7Years              112076 non-null  float64
 38  PublicRecordsLast10Years             112369 non-null  float64
 39  PublicRecordsLast12Months            105462 non-null  float64
 40  RevolvingCreditBalance               105462 non-null  float64
 41  BankcardUtilization                  105462 non-null  float64
 42  AvailableBankcardCredit              105522 non-null  float64
 43  TotalTrades                          105522 non-null  float64
 44  TradesNeverDelinquent (percentage)   105522 non-null  float64
 45  TradesOpenedLast6Months              105522 non-null  float64
 46  DebtToIncomeRatio                    104594 non-null  float64
 47  IncomeRange                          113066 non-null  object 
 48  IncomeVerifiable                     113066 non-null  bool   
 49  StatedMonthlyIncome                  113066 non-null  float64
 50  LoanKey                              113066 non-null  object 
 51  TotalProsperLoans                    21923 non-null   float64
 52  TotalProsperPaymentsBilled           21923 non-null   float64
 53  OnTimeProsperPayments                21923 non-null   float64
 54  ProsperPaymentsLessThanOneMonthLate  21923 non-null   float64
 55  ProsperPaymentsOneMonthPlusLate      21923 non-null   float64
 56  ProsperPrincipalBorrowed             21923 non-null   float64
 57  ProsperPrincipalOutstanding          21923 non-null   float64
 58  ScorexChangeAtTimeOfListing          18912 non-null   float64
 59  LoanCurrentDaysDelinquent            113066 non-null  int64  
 60  LoanFirstDefaultedCycleNumber        16952 non-null   float64
 61  LoanMonthsSinceOrigination           113066 non-null  int64  
 62  LoanNumber                           113066 non-null  int64  
 63  LoanOriginalAmount                   113066 non-null  int64  
 64  LoanOriginationDate                  113066 non-null  object 
 65  LoanOriginationQuarter               113066 non-null  object 
 66  MemberKey                            113066 non-null  object 
 67  MonthlyLoanPayment                   113066 non-null  float64
 68  LP_CustomerPayments                  113066 non-null  float64
 69  LP_CustomerPrincipalPayments         113066 non-null  float64
 70  LP_InterestandFees                   113066 non-null  float64
 71  LP_ServiceFees                       113066 non-null  float64
 72  LP_CollectionFees                    113066 non-null  float64
 73  LP_GrossPrincipalLoss                113066 non-null  float64
 74  LP_NetPrincipalLoss                  113066 non-null  float64
 75  LP_NonPrincipalRecoverypayments      113066 non-null  float64
 76  PercentFunded                        113066 non-null  float64
 77  Recommendations                      113066 non-null  int64  
 78  InvestmentFromFriendsCount           113066 non-null  int64  
 79  InvestmentFromFriendsAmount          113066 non-null  float64
 80  Investors                            113066 non-null  int64  
dtypes: bool(3), float64(50), int64(11), object(17)
memory usage: 68.5+ MB


#profile = ProfileReport(prosper_df, title="Prosper Data Profiling Report", minimal=True)
#profile.to_file("Prosper Data Profiling Report.html")
#profile


# this variable subset will be a starting point for further cleaning and investigation of relevant data
prosper_reduced_data = prosper_df[['ListingKey', 'ListingCreationDate', 'ClosedDate', 'LoanStatus', 'Term', 'LoanOriginalAmount', 'MonthlyLoanPayment',\
              'ListingCategory (numeric)', 'BorrowerAPR', 'BorrowerRate', \
              'CreditGrade', 'ProsperRating (Alpha)', 'CreditScoreRangeLower', 'CreditScoreRangeUpper',\
              'IncomeRange', 'IncomeVerifiable', 'StatedMonthlyIncome', 'DebtToIncomeRatio', 'EmploymentStatus',\
              'EmploymentStatusDuration','IsBorrowerHomeowner']]
prosper_reduced_data.head()


prosper_reduced_data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 113066 entries, 0 to 113936
Data columns (total 21 columns):
 #   Column                     Non-Null Count   Dtype  
---  ------                     --------------   -----  
 0   ListingKey                 113066 non-null  object 
 1   ListingCreationDate        113066 non-null  object 
 2   ClosedDate                 55076 non-null   object 
 3   LoanStatus                 113066 non-null  object 
 4   Term                       113066 non-null  int64  
 5   LoanOriginalAmount         113066 non-null  int64  
 6   MonthlyLoanPayment         113066 non-null  float64
 7   ListingCategory (numeric)  113066 non-null  int64  
 8   BorrowerAPR                113041 non-null  float64
 9   BorrowerRate               113066 non-null  float64
 10  CreditGrade                28953 non-null   object 
 11  ProsperRating (Alpha)      83982 non-null   object 
 12  CreditScoreRangeLower      112475 non-null  float64
 13  CreditScoreRangeUpper      112475 non-null  float64
 14  IncomeRange                113066 non-null  object 
 15  IncomeVerifiable           113066 non-null  bool   
 16  StatedMonthlyIncome        113066 non-null  float64
 17  DebtToIncomeRatio          104594 non-null  float64
 18  EmploymentStatus           110811 non-null  object 
 19  EmploymentStatusDuration   105441 non-null  float64
 20  IsBorrowerHomeowner        113066 non-null  bool   
dtypes: bool(2), float64(8), int64(3), object(8)
memory usage: 17.5+ MB


prosper_reduced_data.describe(include= 'all')


#profile = ProfileReport(prosper_reduced_data, title="Prosper Data Profiling Report", explorative=True)
#profile.to_file("Reduced Prosper Data Profiling Report.html")
#profile


#Convert Listing Creation date and Closed Date to datetime

prosper_reduced_data.ListingCreationDate = pd.to_datetime(prosper_reduced_data.ListingCreationDate,  errors='coerce')
prosper_reduced_data.ClosedDate = pd.to_datetime(prosper_reduced_data.ClosedDate,  errors='coerce')


prosper_reduced_data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 113066 entries, 0 to 113936
Data columns (total 21 columns):
 #   Column                     Non-Null Count   Dtype         
---  ------                     --------------   -----         
 0   ListingKey                 113066 non-null  object        
 1   ListingCreationDate        113066 non-null  datetime64[ns]
 2   ClosedDate                 55076 non-null   datetime64[ns]
 3   LoanStatus                 113066 non-null  object        
 4   Term                       113066 non-null  int64         
 5   LoanOriginalAmount         113066 non-null  int64         
 6   MonthlyLoanPayment         113066 non-null  float64       
 7   ListingCategory (numeric)  113066 non-null  int64         
 8   BorrowerAPR                113041 non-null  float64       
 9   BorrowerRate               113066 non-null  float64       
 10  CreditGrade                28953 non-null   object        
 11  ProsperRating (Alpha)      83982 non-null   object        
 12  CreditScoreRangeLower      112475 non-null  float64       
 13  CreditScoreRangeUpper      112475 non-null  float64       
 14  IncomeRange                113066 non-null  object        
 15  IncomeVerifiable           113066 non-null  bool          
 16  StatedMonthlyIncome        113066 non-null  float64       
 17  DebtToIncomeRatio          104594 non-null  float64       
 18  EmploymentStatus           110811 non-null  object        
 19  EmploymentStatusDuration   105441 non-null  float64       
 20  IsBorrowerHomeowner        113066 non-null  bool          
dtypes: bool(2), datetime64[ns](2), float64(8), int64(3), object(6)
memory usage: 17.5+ MB


#change column names with spaces
prosper_reduced_data.rename(columns={'ListingCategory (numeric)': 'ListingCategory', 'ProsperRating (Alpha)': 'ProsperRating'}, inplace=True)
prosper_reduced_data.head()


#checking on missing values in Employment status
prosper_reduced_data.EmploymentStatus.value_counts()

Employed         66598
Full-time        26354
Self-employed     6052
Not available     5347
Other             3742
Part-time         1088
Not employed       835
Retired            795
Name: EmploymentStatus, dtype: int64


prosper_reduced_data[prosper_reduced_data.EmploymentStatus.isna()].sort_values(by = 'ListingCreationDate')


df = prosper_reduced_data[prosper_reduced_data.EmploymentStatus.isna()].sort_values(by = 'ListingCreationDate')
df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 2255 entries, 113921 to 7312
Data columns (total 21 columns):
 #   Column                    Non-Null Count  Dtype         
---  ------                    --------------  -----         
 0   ListingKey                2255 non-null   object        
 1   ListingCreationDate       2255 non-null   datetime64[ns]
 2   ClosedDate                2255 non-null   datetime64[ns]
 3   LoanStatus                2255 non-null   object        
 4   Term                      2255 non-null   int64         
 5   LoanOriginalAmount        2255 non-null   int64         
 6   MonthlyLoanPayment        2255 non-null   float64       
 7   ListingCategory           2255 non-null   int64         
 8   BorrowerAPR               2230 non-null   float64       
 9   BorrowerRate              2255 non-null   float64       
 10  CreditGrade               2255 non-null   object        
 11  ProsperRating             0 non-null      object        
 12  CreditScoreRangeLower     1666 non-null   float64       
 13  CreditScoreRangeUpper     1666 non-null   float64       
 14  IncomeRange               2255 non-null   object        
 15  IncomeVerifiable          2255 non-null   bool          
 16  StatedMonthlyIncome       2255 non-null   float64       
 17  DebtToIncomeRatio         2226 non-null   float64       
 18  EmploymentStatus          0 non-null      object        
 19  EmploymentStatusDuration  0 non-null      float64       
 20  IsBorrowerHomeowner       2255 non-null   bool          
dtypes: bool(2), datetime64[ns](2), float64(8), int64(3), object(6)
memory usage: 356.7+ KB


df.LoanStatus.value_counts()

Completed     1439
Defaulted      522
Chargedoff     291
Cancelled        3
Name: LoanStatus, dtype: int64


#get no. of listings without Employment Status
len(prosper_reduced_data[prosper_reduced_data.EmploymentStatus.isna()])

2255


prosper_reduced_data = prosper_reduced_data[prosper_reduced_data.EmploymentStatus.notna()]
prosper_reduced_data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 110811 entries, 0 to 113936
Data columns (total 21 columns):
 #   Column                    Non-Null Count   Dtype         
---  ------                    --------------   -----         
 0   ListingKey                110811 non-null  object        
 1   ListingCreationDate       110811 non-null  datetime64[ns]
 2   ClosedDate                52821 non-null   datetime64[ns]
 3   LoanStatus                110811 non-null  object        
 4   Term                      110811 non-null  int64         
 5   LoanOriginalAmount        110811 non-null  int64         
 6   MonthlyLoanPayment        110811 non-null  float64       
 7   ListingCategory           110811 non-null  int64         
 8   BorrowerAPR               110811 non-null  float64       
 9   BorrowerRate              110811 non-null  float64       
 10  CreditGrade               26698 non-null   object        
 11  ProsperRating             83982 non-null   object        
 12  CreditScoreRangeLower     110809 non-null  float64       
 13  CreditScoreRangeUpper     110809 non-null  float64       
 14  IncomeRange               110811 non-null  object        
 15  IncomeVerifiable          110811 non-null  bool          
 16  StatedMonthlyIncome       110811 non-null  float64       
 17  DebtToIncomeRatio         102368 non-null  float64       
 18  EmploymentStatus          110811 non-null  object        
 19  EmploymentStatusDuration  105441 non-null  float64       
 20  IsBorrowerHomeowner       110811 non-null  bool          
dtypes: bool(2), datetime64[ns](2), float64(8), int64(3), object(6)
memory usage: 17.1+ MB


#
prosper_reduced_data.IncomeRange.value_counts()

$25,000-49,999    31940
$50,000-74,999    30749
$100,000+         17188
$75,000-99,999    16780
$1-24,999          7241
Not displayed      5486
Not employed        806
$0                  621
Name: IncomeRange, dtype: int64


# reduce no. of IncomeRange categories by 1
prosper_reduced_data.IncomeRange.replace({'$0': 'Not employed'}, inplace=True)


#test above code
prosper_reduced_data.IncomeRange.value_counts()

$25,000-49,999    31940
$50,000-74,999    30749
$100,000+         17188
$75,000-99,999    16780
$1-24,999          7241
Not displayed      5486
Not employed       1427
Name: IncomeRange, dtype: int64


prosper_reduced_data.CreditGrade.value_counts()

C     5282
D     4799
B     4149
AA    3296
A     3128
HR    3061
E     2884
NC      99
Name: CreditGrade, dtype: int64


prosper_reduced_data.ProsperRating.value_counts()

C     18096
B     15368
A     14390
D     14170
E      9716
HR     6917
AA     5325
Name: ProsperRating, dtype: int64


prosper_reduced_data[['ListingKey','CreditGrade', 'ProsperRating']].sample(30)


prosper_reduced_data['CreditRiskScore'] = np.where(prosper_reduced_data.CreditGrade.isna(), prosper_reduced_data.ProsperRating, prosper_reduced_data.CreditGrade)
prosper_reduced_data.head(10)


prosper_reduced_data.CreditRiskScore.value_counts()

C     23378
B     19517
D     18969
A     17518
E     12600
HR     9978
AA     8621
NC       99
Name: CreditRiskScore, dtype: int64


# the other two ratings columns will not be needed anymore
prosper_reduced_data.drop(['CreditGrade', 'ProsperRating'], axis=1, inplace=True)


prosper_reduced_data


#Employment Duration
borrowers_without_employment_duration_df = prosper_reduced_data[prosper_reduced_data.EmploymentStatusDuration.isnull() == True]
borrowers_without_employment_duration_df


print(f'min Date without employment duration is {borrowers_without_employment_duration_df.ListingCreationDate.min()} and max date was {borrowers_without_employment_duration_df.ListingCreationDate.max()}')

min Date without employment duration is 2006-02-15 14:21:25.347000 and max date was 2013-09-16 14:47:16.037000


borrowers_without_employment_duration_df.EmploymentStatus.value_counts()

Not available    5347
Full-time          13
Other               6
Self-employed       2
Not employed        1
Employed            1
Name: EmploymentStatus, dtype: int64


#non-available borrowers without employment status in raw data before filtering 
prosper_reduced_data[prosper_reduced_data.EmploymentStatus == 'Not available']['EmploymentStatusDuration'].head()

2    NaN
17   NaN
42   NaN
63   NaN
76   NaN
Name: EmploymentStatusDuration, dtype: float64


#most of the borrowers didn't give employmentStatus when onboarded, I will let it as it is


# Convert CreditGrade,IncomeRange as ordered categorical vars
var_dict = {'CreditRiskScore': ['AA', 'A', 'B', 'C', 'D', 'E', 'HR', 'NC'],
           'IncomeRange': ['Not displayed', 'Not employed', '$1-24,999', 
                           '$25,000-49,999', '$50,000-74,999', '$75,000-99,999', '$100,000+']}
for var in var_dict:
    ordered_var = pd.api.types.CategoricalDtype(ordered=True, categories = var_dict[var])
    prosper_reduced_data[var] = prosper_reduced_data[var].astype(ordered_var)


prosper_reduced_data.head()


prosper_reduced_data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 110811 entries, 0 to 113936
Data columns (total 20 columns):
 #   Column                    Non-Null Count   Dtype         
---  ------                    --------------   -----         
 0   ListingKey                110811 non-null  object        
 1   ListingCreationDate       110811 non-null  datetime64[ns]
 2   ClosedDate                52821 non-null   datetime64[ns]
 3   LoanStatus                110811 non-null  object        
 4   Term                      110811 non-null  int64         
 5   LoanOriginalAmount        110811 non-null  int64         
 6   MonthlyLoanPayment        110811 non-null  float64       
 7   ListingCategory           110811 non-null  int64         
 8   BorrowerAPR               110811 non-null  float64       
 9   BorrowerRate              110811 non-null  float64       
 10  CreditScoreRangeLower     110809 non-null  float64       
 11  CreditScoreRangeUpper     110809 non-null  float64       
 12  IncomeRange               110811 non-null  category      
 13  IncomeVerifiable          110811 non-null  bool          
 14  StatedMonthlyIncome       110811 non-null  float64       
 15  DebtToIncomeRatio         102368 non-null  float64       
 16  EmploymentStatus          110811 non-null  object        
 17  EmploymentStatusDuration  105441 non-null  float64       
 18  IsBorrowerHomeowner       110811 non-null  bool          
 19  CreditRiskScore           110680 non-null  category      
dtypes: bool(2), category(2), datetime64[ns](2), float64(8), int64(3), object(3)
memory usage: 14.8+ MB


prosper_reduced_data['ListingCategory'] = prosper_reduced_data['ListingCategory'].astype('category')
prosper_reduced_data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 110811 entries, 0 to 113936
Data columns (total 20 columns):
 #   Column                    Non-Null Count   Dtype         
---  ------                    --------------   -----         
 0   ListingKey                110811 non-null  object        
 1   ListingCreationDate       110811 non-null  datetime64[ns]
 2   ClosedDate                52821 non-null   datetime64[ns]
 3   LoanStatus                110811 non-null  object        
 4   Term                      110811 non-null  int64         
 5   LoanOriginalAmount        110811 non-null  int64         
 6   MonthlyLoanPayment        110811 non-null  float64       
 7   ListingCategory           110811 non-null  category      
 8   BorrowerAPR               110811 non-null  float64       
 9   BorrowerRate              110811 non-null  float64       
 10  CreditScoreRangeLower     110809 non-null  float64       
 11  CreditScoreRangeUpper     110809 non-null  float64       
 12  IncomeRange               110811 non-null  category      
 13  IncomeVerifiable          110811 non-null  bool          
 14  StatedMonthlyIncome       110811 non-null  float64       
 15  DebtToIncomeRatio         102368 non-null  float64       
 16  EmploymentStatus          110811 non-null  object        
 17  EmploymentStatusDuration  105441 non-null  float64       
 18  IsBorrowerHomeowner       110811 non-null  bool          
 19  CreditRiskScore           110680 non-null  category      
dtypes: bool(2), category(3), datetime64[ns](2), float64(8), int64(2), object(3)
memory usage: 14.1+ MB


#get histogram for all numerical columns
numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']

prosper_reduced_data_numerical = prosper_reduced_data.select_dtypes(include=numerics)
prosper_reduced_data_numerical


list(prosper_reduced_data_numerical.columns)

['Term',
 'LoanOriginalAmount',
 'MonthlyLoanPayment',
 'BorrowerAPR',
 'BorrowerRate',
 'CreditScoreRangeLower',
 'CreditScoreRangeUpper',
 'StatedMonthlyIncome',
 'DebtToIncomeRatio',
 'EmploymentStatusDuration']


plt.rcParams['figure.figsize'] = (20, 10)


#histograms
prosper_reduced_data_numerical.hist(list(prosper_reduced_data_numerical.columns));


#get Categorical Columns. 

prosper_reduced_data_object = prosper_reduced_data.select_dtypes(include=['object', 'category'])
#exclude listingid
prosper_reduced_data_object = prosper_reduced_data_object.iloc[:, 1:]
prosper_reduced_data_object


#plot countplots 
def countplotfunc(df, col):
    
    plt.rcParams["figure.figsize"] = (20,7)
    counts = df[col].value_counts()
    status_order = counts.index
    max_count = counts[0]
    max_prop = max_count / df.shape[0]
    tick_props = np.arange(0, max_prop, 0.1)
    tick_names = ['{:0.2f}'.format(v) for v in tick_props]
    
    title = col + ' Value Distribution'
    ax = sns.countplot(data = df, x = col, order = status_order, color = 'blue').set(title = title)
    plt.xticks(rotation = 45)
    
    # values must be ordered from the largest for this to work
    for i in range(counts.shape[0]):
        count = counts[i]
        pct_string = '{:0.1f}%'.format(100 * count / df.shape[0])
        plt.text(i, count+10, pct_string, ha = 'center')

    plt.tight_layout();


for col in list(prosper_reduced_data_object):
    countplotfunc(prosper_reduced_data_object, col)
    plt.show()


# Boolean columns
prosper_boolean = prosper_reduced_data.select_dtypes(include=bool)


def piecharts(df, col):
    df[col].value_counts().plot(kind = 'pie', autopct='%1.0f%%');
    plt.show()


for col in list(prosper_boolean.columns):
    piecharts(prosper_boolean, col)


sns.distplot(prosper_reduced_data['MonthlyLoanPayment']);


prosper_reduced_data['MonthlyLoanPayment'].describe()

count    110811.000000
mean        274.236988
std         192.719850
min           0.000000
25%         134.020000
50%         220.730000
75%         372.710000
max        2251.510000
Name: MonthlyLoanPayment, dtype: float64


plt.figure(figsize=(13,4))
plt.suptitle("Credit Score Range")
plt.subplot(1,2,1)
bins_lower = np.arange(0, df.CreditScoreRangeLower.max()+20, 20)
plt.hist(data = prosper_reduced_data, x = 'CreditScoreRangeLower', bins = bins_lower)
plt.xlabel('CreditScoreRangeLower')
plt.subplot(1,2,2)
bins_upper = np.arange(0, df.CreditScoreRangeUpper.max()+20, 20)
plt.hist(data = prosper_reduced_data, x = 'CreditScoreRangeUpper', bins = bins_upper)
plt.xlabel('CreditScoreRangeUpper');


prosper_reduced_data[['CreditScoreRangeLower', 'CreditScoreRangeUpper']].describe()


prosper_reduced_data.drop('CreditScoreRangeUpper', axis = 1, inplace = True)


prosper_reduced_data.rename(columns = {'CreditScoreRangeLower': 'CreditScoreRange'})


prosper_reduced_data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 110811 entries, 0 to 113936
Data columns (total 19 columns):
 #   Column                    Non-Null Count   Dtype         
---  ------                    --------------   -----         
 0   ListingKey                110811 non-null  object        
 1   ListingCreationDate       110811 non-null  datetime64[ns]
 2   ClosedDate                52821 non-null   datetime64[ns]
 3   LoanStatus                110811 non-null  object        
 4   Term                      110811 non-null  int64         
 5   LoanOriginalAmount        110811 non-null  int64         
 6   MonthlyLoanPayment        110811 non-null  float64       
 7   ListingCategory           110811 non-null  category      
 8   BorrowerAPR               110811 non-null  float64       
 9   BorrowerRate              110811 non-null  float64       
 10  CreditScoreRangeLower     110809 non-null  float64       
 11  IncomeRange               110811 non-null  category      
 12  IncomeVerifiable          110811 non-null  bool          
 13  StatedMonthlyIncome       110811 non-null  float64       
 14  DebtToIncomeRatio         102368 non-null  float64       
 15  EmploymentStatus          110811 non-null  object        
 16  EmploymentStatusDuration  105441 non-null  float64       
 17  IsBorrowerHomeowner       110811 non-null  bool          
 18  CreditRiskScore           110680 non-null  category      
dtypes: bool(2), category(3), datetime64[ns](2), float64(7), int64(2), object(3)
memory usage: 13.2+ MB


prosper_reduced_data


prosper_reduced_data.ListingCreationDate.describe()

count                         110811
unique                        110809
top       2012-10-20 12:21:46.333000
freq                               2
first     2006-02-15 14:21:25.347000
last      2014-03-10 12:20:53.760000
Name: ListingCreationDate, dtype: object


prosper_reduced_data.groupby(prosper_reduced_data.ListingCreationDate.dt.year)['ListingKey'].count().plot(kind = 'bar');
plt.ylabel('Number of loans');


# 2009 was a critical year because of the financial crisis


prosper_reduced_data.ClosedDate.describe()

count                   52821
unique                   2682
top       2014-03-04 00:00:00
freq                      105
first     2006-05-10 00:00:00
last      2014-03-10 00:00:00
Name: ClosedDate, dtype: object


#we have 57990 accounts still open
prosper_reduced_data.ClosedDate.isnull().sum()

57990


# months between loan origination and closing date
prosper_reduced_data['MonthsToClosed'] = ((prosper_reduced_data.ClosedDate - prosper_reduced_data.ListingCreationDate) / np.timedelta64(1, 'M'))


prosper_reduced_data['MonthsToClosed'].describe()

count    52821.000000
mean        19.656067
std         11.684394
min        -45.421942
25%          9.645909
50%         17.455657
75%         31.265634
max         66.324018
Name: MonthsToClosed, dtype: float64


# we have accounts which have months to close as  negative which is errornous


prosper_reduced_data[prosper_reduced_data['MonthsToClosed'] < 0]


prosper_reduced_data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 110811 entries, 0 to 113936
Data columns (total 20 columns):
 #   Column                    Non-Null Count   Dtype         
---  ------                    --------------   -----         
 0   ListingKey                110811 non-null  object        
 1   ListingCreationDate       110811 non-null  datetime64[ns]
 2   ClosedDate                52821 non-null   datetime64[ns]
 3   LoanStatus                110811 non-null  object        
 4   Term                      110811 non-null  int64         
 5   LoanOriginalAmount        110811 non-null  int64         
 6   MonthlyLoanPayment        110811 non-null  float64       
 7   ListingCategory           110811 non-null  category      
 8   BorrowerAPR               110811 non-null  float64       
 9   BorrowerRate              110811 non-null  float64       
 10  CreditScoreRangeLower     110809 non-null  float64       
 11  IncomeRange               110811 non-null  category      
 12  IncomeVerifiable          110811 non-null  bool          
 13  StatedMonthlyIncome       110811 non-null  float64       
 14  DebtToIncomeRatio         102368 non-null  float64       
 15  EmploymentStatus          110811 non-null  object        
 16  EmploymentStatusDuration  105441 non-null  float64       
 17  IsBorrowerHomeowner       110811 non-null  bool          
 18  CreditRiskScore           110680 non-null  category      
 19  MonthsToClosed            52821 non-null   float64       
dtypes: bool(2), category(3), datetime64[ns](2), float64(8), int64(2), object(3)
memory usage: 14.1+ MB


# This account creation Date is less than Closed Date. We will drop it from our analysis


prosper_reduced_data_check = prosper_reduced_data[prosper_reduced_data['MonthsToClosed'] > 0]


prosper_reduced_data['MonthsToClosed'].describe()

count    52821.000000
mean        19.656067
std         11.684394
min        -45.421942
25%          9.645909
50%         17.455657
75%         31.265634
max         66.324018
Name: MonthsToClosed, dtype: float64


prosper_reduced_data.columns

Index(['ListingKey', 'ListingCreationDate', 'ClosedDate', 'LoanStatus', 'Term',
       'LoanOriginalAmount', 'MonthlyLoanPayment', 'ListingCategory',
       'BorrowerAPR', 'BorrowerRate', 'CreditScoreRangeLower', 'IncomeRange',
       'IncomeVerifiable', 'StatedMonthlyIncome', 'DebtToIncomeRatio',
       'EmploymentStatus', 'EmploymentStatusDuration', 'IsBorrowerHomeowner',
       'CreditRiskScore', 'MonthsToClosed'],
      dtype='object')


#dropping date columns, interestRate and listingkey since we won't need them further
prosper_reduced_data.drop(columns = ['ListingKey','BorrowerRate', 'ListingCreationDate', 'ClosedDate'], inplace = True)


prosper_reduced_data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 110811 entries, 0 to 113936
Data columns (total 16 columns):
 #   Column                    Non-Null Count   Dtype   
---  ------                    --------------   -----   
 0   LoanStatus                110811 non-null  object  
 1   Term                      110811 non-null  int64   
 2   LoanOriginalAmount        110811 non-null  int64   
 3   MonthlyLoanPayment        110811 non-null  float64 
 4   ListingCategory           110811 non-null  category
 5   BorrowerAPR               110811 non-null  float64 
 6   CreditScoreRangeLower     110809 non-null  float64 
 7   IncomeRange               110811 non-null  category
 8   IncomeVerifiable          110811 non-null  bool    
 9   StatedMonthlyIncome       110811 non-null  float64 
 10  DebtToIncomeRatio         102368 non-null  float64 
 11  EmploymentStatus          110811 non-null  object  
 12  EmploymentStatusDuration  105441 non-null  float64 
 13  IsBorrowerHomeowner       110811 non-null  bool    
 14  CreditRiskScore           110680 non-null  category
 15  MonthsToClosed            52821 non-null   float64 
dtypes: bool(2), category(3), float64(7), int64(2), object(2)
memory usage: 10.7+ MB


prosper_reduced_data_samp =  prosper_reduced_data.sample(frac = 0.3, random_state = 1) 
prosper_reduced_data_samp_numerical = prosper_reduced_data_samp.select_dtypes(include=numerics)


sns.pairplot(prosper_reduced_data_samp_numerical);


#Correlation matrix
sns.heatmap(prosper_reduced_data_samp_numerical.corr(), annot = True);


def ScatterPlots(df, col):
    sns.regplot(data = df, x = df[col], y = df.BorrowerAPR, scatter_kws={'alpha':0.3}, y_jitter = 0.2)
    plt.show()


# With Outliers
correlated_cols = ['LoanOriginalAmount', 'MonthlyLoanPayment','CreditScoreRangeLower']

for col in correlated_cols:
    ScatterPlots(prosper_reduced_data_samp_numerical, col)


#Capping outliers so as to get a clear view of APR vs Correlated variables
def cap_data(df):
    for col in df.columns:
        print("capping the ",col)
        if (((df[col].dtype)=='float64') | ((df[col].dtype)=='int64')):
            percentiles = df[col].quantile([0.01,0.99]).values
            df[col][df[col] <= percentiles[0]] = percentiles[0]
            df[col][df[col] >= percentiles[1]] = percentiles[1]
        else:
            df[col]=df[col]
    return df

final_df=cap_data(prosper_reduced_data_samp_numerical)

capping the  Term
capping the  LoanOriginalAmount
capping the  MonthlyLoanPayment
capping the  BorrowerAPR
capping the  CreditScoreRangeLower
capping the  StatedMonthlyIncome
capping the  DebtToIncomeRatio
capping the  EmploymentStatusDuration
capping the  MonthsToClosed


for col in correlated_cols:
    ScatterPlots(final_df, col)


def ScatterPlots(df, col, y_axis):
    sns.regplot(data = df, x = df[col], y = df[y_axis], scatter_kws={'alpha':0.3}, y_jitter = 0.2)
    plt.show()


correlated_cols = ['MonthlyLoanPayment', 'CreditScoreRangeLower', 'StatedMonthlyIncome']
for col in correlated_cols:
    ScatterPlots(final_df, col, 'LoanOriginalAmount')


prosper_reduced_data_samp


# plot matrix of numeric features against categorical features.

def boxgrid(x, y, **kwargs):
    """ Quick hack for creating box plots with seaborn's PairGrid. """
    default_color = sns.color_palette()[0]
    sns.boxplot(x, y, color = default_color)

plt.figure(figsize = [12, 20])
g = sns.PairGrid(data = prosper_reduced_data_samp, x_vars = ['BorrowerAPR', 'StatedMonthlyIncome', 'LoanOriginalAmount'], 
                y_vars = prosper_reduced_data_object.columns, size = 3, aspect = 1.5)
g.map(boxgrid);
plt.xticks(rotation=45);

<Figure size 864x1440 with 0 Axes>


g = sns.FacetGrid(data = prosper_reduced_data_samp, col = 'IncomeVerifiable')
g.map(sns.distplot, 'BorrowerAPR');


g = sns.FacetGrid(data = prosper_reduced_data_samp, col = 'IsBorrowerHomeowner')
g.map(sns.distplot, 'BorrowerAPR');


prosper_reduced_data_samp_numerical.columns

Index(['Term', 'LoanOriginalAmount', 'MonthlyLoanPayment', 'BorrowerAPR',
       'CreditScoreRangeLower', 'StatedMonthlyIncome', 'DebtToIncomeRatio',
       'EmploymentStatusDuration', 'MonthsToClosed'],
      dtype='object')


plt.figure(figsize=(13,4))

<Figure size 936x288 with 0 Axes>

<Figure size 936x288 with 0 Axes>


# Pair plot colored by credit risk
sns.pairplot(prosper_reduced_data_samp, 
             vars = prosper_reduced_data_samp_numerical.columns, 
             hue = 'CreditRiskScore', diag_kind = 'kde', palette = 'RdYlBu_r',
             plot_kws = {'alpha': 0.6, 's': 90},
             size = 4);


plt.figure(figsize=(16,15))
g = sns.FacetGrid(data = prosper_reduced_data_samp, col = 'CreditRiskScore', hue = 'Term');
g.map(sns.scatterplot, 'LoanOriginalAmount', 'BorrowerAPR');
g.add_legend()
plt.show();

<Figure size 1152x1080 with 0 Axes>


plt.figure(figsize=(16,15))
g = sns.FacetGrid(data = prosper_reduced_data_samp, col = 'CreditRiskScore', hue = 'Term');
g.map(sns.scatterplot, 'DebtToIncomeRatio' , 'BorrowerAPR');
g.add_legend()
plt.show();

<Figure size 1152x1080 with 0 Axes>


sns.catplot(x = 'EmploymentStatus', y = 'BorrowerAPR', hue = 'CreditRiskScore', order = \
           ['Full-time','Employed','Part-time','Self-employed','Retired','Other','Not employed','Not available'], \
           kind = 'box', data = prosper_reduced_data_samp, size = 15, palette = 'RdYlBu_r');


sns.catplot(x = 'EmploymentStatus', y = 'BorrowerAPR', hue = 'IsBorrowerHomeowner', order = \
           ['Full-time','Employed','Part-time','Self-employed','Retired','Other','Not employed','Not available'], \
           kind = 'box', data = prosper_reduced_data_samp, size = 15, palette = 'colorblind');


fig = plt.figure(figsize = [8,6])
ax = sns.pointplot(data = prosper_reduced_data_samp, x = 'c', y = 'BorrowerAPR', hue = 'Term',
           palette = 'Blues', linestyles = '', dodge = 0.4, ci='sd')
plt.title('Borrower APR across rating and term')
plt.ylabel('Mean Borrower APR')
ax.set_yticklabels([],minor = True);


sns.pointplot(data = prosper_reduced_data_samp, x = 'CreditRiskScore', y = 'LoanOriginalAmount', hue = 'Term',
           palette = 'Greens', linestyles = '', dodge = 0.4);

	ListingKey	ListingNumber	ListingCreationDate	CreditGrade	Term	LoanStatus	ClosedDate	BorrowerAPR	BorrowerRate	LenderYield	EstimatedEffectiveYield	EstimatedLoss	EstimatedReturn	ProsperRating (numeric)	ProsperRating (Alpha)	ProsperScore	ListingCategory (numeric)	BorrowerState	Occupation	EmploymentStatus	EmploymentStatusDuration	IsBorrowerHomeowner	CurrentlyInGroup	GroupKey	DateCreditPulled	CreditScoreRangeLower	CreditScoreRangeUpper	FirstRecordedCreditLine	CurrentCreditLines	OpenCreditLines	TotalCreditLinespast7years	OpenRevolvingAccounts	OpenRevolvingMonthlyPayment	InquiriesLast6Months	TotalInquiries	CurrentDelinquencies	AmountDelinquent	DelinquenciesLast7Years	PublicRecordsLast10Years	PublicRecordsLast12Months	RevolvingCreditBalance	BankcardUtilization	AvailableBankcardCredit	TotalTrades	TradesNeverDelinquent (percentage)	TradesOpenedLast6Months	DebtToIncomeRatio	IncomeRange	IncomeVerifiable	StatedMonthlyIncome	LoanKey	TotalProsperLoans	TotalProsperPaymentsBilled	OnTimeProsperPayments	ProsperPaymentsLessThanOneMonthLate	ProsperPaymentsOneMonthPlusLate	ProsperPrincipalBorrowed	ProsperPrincipalOutstanding	ScorexChangeAtTimeOfListing	LoanCurrentDaysDelinquent	LoanFirstDefaultedCycleNumber	LoanMonthsSinceOrigination	LoanNumber	LoanOriginalAmount	LoanOriginationDate	LoanOriginationQuarter	MemberKey	MonthlyLoanPayment	LP_CustomerPayments	LP_CustomerPrincipalPayments	LP_InterestandFees	LP_ServiceFees	LP_CollectionFees	LP_GrossPrincipalLoss	LP_NetPrincipalLoss	LP_NonPrincipalRecoverypayments	PercentFunded	Recommendations	InvestmentFromFriendsCount	InvestmentFromFriendsAmount	Investors
0	1021339766868145413AB3B	193129	2007-08-26 19:09:29.263000000	C	36	Completed	2009-08-14 00:00:00	0.16516	0.1580	0.1380	NaN	NaN	NaN	NaN	NaN	NaN	0	CO	Other	Self-employed	2.0	True	True	NaN	2007-08-26 18:41:46.780000000	640.0	659.0	2001-10-11 00:00:00	5.0	4.0	12.0	1	24.0	3.0	3.0	2.0	472.0	4.0	0.0	0.0	0.0	0.00	1500.0	11.0	0.81	0.0	0.17	$25,000-49,999	True	3083.333333	E33A3400205839220442E84	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0	NaN	78	19141	9425	2007-09-12 00:00:00	Q3 2007	1F3E3376408759268057EDA	330.43	11396.1400	9425.00	1971.1400	-133.18	0.0	0.0	0.0	0.0	1.0	0	0	0.0	258
1	10273602499503308B223C1	1209647	2014-02-27 08:28:07.900000000	NaN	36	Current	NaN	0.12016	0.0920	0.0820	0.07960	0.0249	0.05470	6.0	A	7.0	2	CO	Professional	Employed	44.0	False	False	NaN	2014-02-27 08:28:14	680.0	699.0	1996-03-18 00:00:00	14.0	14.0	29.0	13	389.0	3.0	5.0	0.0	0.0	0.0	1.0	0.0	3989.0	0.21	10266.0	29.0	1.00	2.0	0.18	$50,000-74,999	True	6125.000000	9E3B37071505919926B1D82	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0	NaN	0	134815	10000	2014-03-03 00:00:00	Q1 2014	1D13370546739025387B2F4	318.93	0.0000	0.00	0.0000	0.00	0.0	0.0	0.0	0.0	1.0	0	0	0.0	1
2	0EE9337825851032864889A	81716	2007-01-05 15:00:47.090000000	HR	36	Completed	2009-12-17 00:00:00	0.28269	0.2750	0.2400	NaN	NaN	NaN	NaN	NaN	NaN	0	GA	Other	Not available	NaN	False	True	783C3371218786870A73D20	2007-01-02 14:09:10.060000000	480.0	499.0	2002-07-27 00:00:00	NaN	NaN	3.0	0	0.0	0.0	1.0	1.0	NaN	0.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.06	Not displayed	True	2083.333333	6954337960046817851BCB2	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0	NaN	86	6466	3001	2007-01-17 00:00:00	Q1 2007	5F7033715035555618FA612	123.32	4186.6300	3001.00	1185.6300	-24.20	0.0	0.0	0.0	0.0	1.0	0	0	0.0	41
3	0EF5356002482715299901A	658116	2012-10-22 11:02:35.010000000	NaN	36	Current	NaN	0.12528	0.0974	0.0874	0.08490	0.0249	0.06000	6.0	A	9.0	16	GA	Skilled Labor	Employed	113.0	True	False	NaN	2012-10-22 11:02:32	800.0	819.0	1983-02-28 00:00:00	5.0	5.0	29.0	7	115.0	0.0	1.0	4.0	10056.0	14.0	0.0	0.0	1444.0	0.04	30754.0	26.0	0.76	0.0	0.15	$25,000-49,999	True	2875.000000	A0393664465886295619C51	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0	NaN	16	77296	10000	2012-11-01 00:00:00	Q4 2012	9ADE356069835475068C6D2	321.45	5143.2000	4091.09	1052.1100	-108.01	0.0	0.0	0.0	0.0	1.0	0	0	0.0	158
4	0F023589499656230C5E3E2	909464	2013-09-14 18:38:39.097000000	NaN	36	Current	NaN	0.24614	0.2085	0.1985	0.18316	0.0925	0.09066	3.0	D	4.0	2	MN	Executive	Employed	44.0	True	False	NaN	2013-09-14 18:38:44	680.0	699.0	2004-02-20 00:00:00	19.0	19.0	49.0	6	220.0	1.0	9.0	0.0	0.0	0.0	0.0	0.0	6193.0	0.81	695.0	39.0	0.95	2.0	0.26	$100,000+	True	9583.333333	A180369302188889200689E	1.0	11.0	11.0	0.0	0.0	11000.0	9947.90	NaN	0	NaN	6	102670	15000	2013-09-20 00:00:00	Q3 2013	36CE356043264555721F06C	563.97	2819.8500	1563.22	1256.6300	-60.27	0.0	0.0	0.0	0.0	1.0	0	0	0.0	20
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
113932	E6D9357655724827169606C	753087	2013-04-14 05:55:02.663000000	NaN	36	Current	NaN	0.22354	0.1864	0.1764	0.16490	0.0699	0.09500	4.0	C	5.0	1	IL	Food Service Management	Employed	246.0	True	False	NaN	2013-04-14 05:54:58	700.0	719.0	1997-09-01 00:00:00	9.0	9.0	41.0	9	209.0	0.0	0.0	0.0	0.0	7.0	1.0	0.0	7714.0	0.80	1886.0	37.0	0.83	3.0	0.13	$50,000-74,999	True	4333.333333	9BD7367919051593140DB62	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0	NaN	11	88485	10000	2013-04-22 00:00:00	Q2 2013	2EC435768441332602FDC15	364.74	3647.4000	2238.38	1409.0200	-75.58	0.0	0.0	0.0	0.0	1.0	0	0	0.0	1
113933	E6DB353036033497292EE43	537216	2011-11-03 20:42:55.333000000	NaN	36	FinalPaymentInProgress	NaN	0.13220	0.1110	0.1010	0.10070	0.0200	0.08070	6.0	A	8.0	7	PA	Professional	Employed	21.0	True	False	NaN	2011-11-03 20:42:53	700.0	719.0	1992-01-17 00:00:00	14.0	13.0	39.0	9	495.0	1.0	4.0	1.0	5062.0	4.0	0.0	0.0	15743.0	0.69	6658.0	39.0	0.92	0.0	0.11	$75,000-99,999	True	8041.666667	62D93634569816897D5A276	3.0	60.0	60.0	0.0	0.0	33501.0	4815.42	-26.0	0	NaN	28	55801	2000	2011-11-07 00:00:00	Q4 2011	55C4336679182766893E4FC	65.57	2330.5500	1997.16	333.3900	-30.05	0.0	0.0	0.0	0.0	1.0	0	0	0.0	22
113934	E6E13596170052029692BB1	1069178	2013-12-13 05:49:12.703000000	NaN	60	Current	NaN	0.23984	0.2150	0.2050	0.18828	0.1025	0.08578	3.0	D	3.0	1	TX	Other	Employed	84.0	True	False	NaN	2013-12-13 05:49:15	700.0	719.0	2002-02-25 00:00:00	12.0	12.0	25.0	9	521.0	1.0	2.0	0.0	0.0	0.0	0.0	0.0	22147.0	0.73	7853.0	25.0	1.00	0.0	0.51	$25,000-49,999	True	2875.000000	DD1A370200396006300ACA0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0	NaN	3	123122	10000	2013-12-23 00:00:00	Q4 2013	0FE0370029359765342FDB5	273.35	546.7000	183.15	363.5500	-16.91	0.0	0.0	0.0	0.0	1.0	0	0	0.0	119
113935	E6EB3531504622671970D9E	539056	2011-11-14 13:18:26.597000000	NaN	60	Completed	2013-08-13 00:00:00	0.28408	0.2605	0.2505	0.24450	0.0850	0.15950	4.0	C	5.0	2	GA	Food Service	Full-time	94.0	True	False	NaN	2011-11-14 13:18:24	680.0	699.0	1993-12-01 00:00:00	11.0	11.0	22.0	7	488.0	1.0	4.0	0.0	0.0	0.0	1.0	0.0	11956.0	0.69	4137.0	19.0	0.80	1.0	0.48	$25,000-49,999	True	3875.000000	589536350469116027ED11B	1.0	16.0	16.0	0.0	0.0	5000.0	3264.37	-4.0	0	NaN	28	56401	15000	2011-11-21 00:00:00	Q4 2011	A33834861822272782621C8	449.55	21122.5600	15000.00	6122.5600	-235.05	0.0	0.0	0.0	0.0	1.0	0	0	0.0	274
113936	E6ED3600409833199F711B7	1140093	2014-01-15 09:27:37.657000000	NaN	36	Current	NaN	0.13189	0.1039	0.0939	0.09071	0.0299	0.06081	6.0	A	7.0	1	NY	Professor	Employed	244.0	False	False	NaN	2014-01-15 09:27:40	680.0	699.0	1995-01-01 00:00:00	10.0	9.0	44.0	8	289.0	0.0	1.0	1.0	257.0	3.0	1.0	0.0	6166.0	0.80	675.0	36.0	0.75	0.0	0.23	$50,000-74,999	True	4583.333333	00AF3704550953269A64E40	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0	NaN	2	127508	2000	2014-01-21 00:00:00	Q1 2014	CE1E3704648000761C9F724	64.90	64.3307	47.25	17.0807	-1.70	0.0	0.0	0.0	0.0	1.0	0	0	0.0	1

	ListingKey	CreditGrade	ProsperRating
3786	0E343581157529075FC619B	NaN	D
28338	BCD635286314316442233F1	NaN	D
40730	97413544990144310CB0DA0	NaN	HR
93957	D51D351501389539177927A	NaN	E
32152	6E693423836664647DDF678	C	NaN
...	...	...	...
21708	F87C353164153037139EB8A	NaN	A
30495	6D2B34200402236278C99FD	C	NaN
2090	733335868973606601F6BB4	NaN	B
69454	E1C33516664169197938BC5	NaN	D
38079	52F4337170122538069DC2C	E	NaN

	ListingKey	ListingCreationDate	ClosedDate	LoanStatus	Term	LoanOriginalAmount	MonthlyLoanPayment	ListingCategory	BorrowerAPR	BorrowerRate	CreditGrade	ProsperRating	CreditScoreRangeLower	CreditScoreRangeUpper	IncomeRange	IncomeVerifiable	StatedMonthlyIncome	DebtToIncomeRatio	EmploymentStatus	EmploymentStatusDuration	IsBorrowerHomeowner	CreditRiskScore
0	1021339766868145413AB3B	2007-08-26 19:09:29.263	2009-08-14	Completed	36	9425	330.43	0	0.16516	0.1580	C	NaN	640.0	659.0	$25,000-49,999	True	3083.333333	0.17	Self-employed	2.0	True	C
1	10273602499503308B223C1	2014-02-27 08:28:07.900	NaT	Current	36	10000	318.93	2	0.12016	0.0920	NaN	A	680.0	699.0	$50,000-74,999	True	6125.000000	0.18	Employed	44.0	False	A
2	0EE9337825851032864889A	2007-01-05 15:00:47.090	2009-12-17	Completed	36	3001	123.32	0	0.28269	0.2750	HR	NaN	480.0	499.0	Not displayed	True	2083.333333	0.06	Not available	NaN	False	HR
3	0EF5356002482715299901A	2012-10-22 11:02:35.010	NaT	Current	36	10000	321.45	16	0.12528	0.0974	NaN	A	800.0	819.0	$25,000-49,999	True	2875.000000	0.15	Employed	113.0	True	A
4	0F023589499656230C5E3E2	2013-09-14 18:38:39.097	NaT	Current	36	15000	563.97	2	0.24614	0.2085	NaN	D	680.0	699.0	$100,000+	True	9583.333333	0.26	Employed	44.0	True	D
5	0F05359734824199381F61D	2013-12-14 08:26:37.093	NaT	Current	60	15000	342.37	1	0.15425	0.1314	NaN	B	740.0	759.0	$100,000+	True	8333.333333	0.36	Employed	82.0	True	B
6	0F0A3576754255009D63151	2013-04-12 09:52:56.147	NaT	Current	36	3000	122.67	1	0.31032	0.2712	NaN	E	680.0	699.0	$25,000-49,999	True	2083.333333	0.27	Employed	172.0	False	E
7	0F1035772717087366F9EA7	2013-05-05 06:49:27.493	NaT	Current	36	10000	372.60	2	0.23939	0.2019	NaN	C	700.0	719.0	$25,000-49,999	True	3355.750000	0.24	Employed	103.0	False	C
8	0F043596202561788EA13D5	2013-12-02 10:43:39.117	NaT	Current	36	10000	305.54	7	0.07620	0.0629	NaN	AA	820.0	839.0	$25,000-49,999	True	3333.333333	0.25	Employed	269.0	True	AA
10	0F123545674891886D9F106	2012-05-10 07:04:01.577	NaT	Current	60	13500	395.37	1	0.27462	0.2489	NaN	C	640.0	659.0	$75,000-99,999	True	7500.000000	0.12	Employed	300.0	False	C

	ListingKey	ListingCreationDate	ClosedDate	LoanStatus	Term	LoanOriginalAmount	MonthlyLoanPayment	ListingCategory	BorrowerAPR	BorrowerRate	CreditScoreRangeLower	CreditScoreRangeUpper	IncomeRange	IncomeVerifiable	StatedMonthlyIncome	DebtToIncomeRatio	EmploymentStatus	EmploymentStatusDuration	IsBorrowerHomeowner	CreditRiskScore
0	1021339766868145413AB3B	2007-08-26 19:09:29.263	2009-08-14	Completed	36	9425	330.43	0	0.16516	0.1580	640.0	659.0	$25,000-49,999	True	3083.333333	0.17	Self-employed	2.0	True	C
1	10273602499503308B223C1	2014-02-27 08:28:07.900	NaT	Current	36	10000	318.93	2	0.12016	0.0920	680.0	699.0	$50,000-74,999	True	6125.000000	0.18	Employed	44.0	False	A
2	0EE9337825851032864889A	2007-01-05 15:00:47.090	2009-12-17	Completed	36	3001	123.32	0	0.28269	0.2750	480.0	499.0	Not displayed	True	2083.333333	0.06	Not available	NaN	False	HR
3	0EF5356002482715299901A	2012-10-22 11:02:35.010	NaT	Current	36	10000	321.45	16	0.12528	0.0974	800.0	819.0	$25,000-49,999	True	2875.000000	0.15	Employed	113.0	True	A
4	0F023589499656230C5E3E2	2013-09-14 18:38:39.097	NaT	Current	36	15000	563.97	2	0.24614	0.2085	680.0	699.0	$100,000+	True	9583.333333	0.26	Employed	44.0	True	D
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
113932	E6D9357655724827169606C	2013-04-14 05:55:02.663	NaT	Current	36	10000	364.74	1	0.22354	0.1864	700.0	719.0	$50,000-74,999	True	4333.333333	0.13	Employed	246.0	True	C
113933	E6DB353036033497292EE43	2011-11-03 20:42:55.333	NaT	FinalPaymentInProgress	36	2000	65.57	7	0.13220	0.1110	700.0	719.0	$75,000-99,999	True	8041.666667	0.11	Employed	21.0	True	A
113934	E6E13596170052029692BB1	2013-12-13 05:49:12.703	NaT	Current	60	10000	273.35	1	0.23984	0.2150	700.0	719.0	$25,000-49,999	True	2875.000000	0.51	Employed	84.0	True	D
113935	E6EB3531504622671970D9E	2011-11-14 13:18:26.597	2013-08-13	Completed	60	15000	449.55	2	0.28408	0.2605	680.0	699.0	$25,000-49,999	True	3875.000000	0.48	Full-time	94.0	True	C
113936	E6ED3600409833199F711B7	2014-01-15 09:27:37.657	NaT	Current	36	2000	64.90	1	0.13189	0.1039	680.0	699.0	$50,000-74,999	True	4583.333333	0.23	Employed	244.0	False	A

	ListingKey	ListingCreationDate	ClosedDate	LoanStatus	Term	LoanOriginalAmount	MonthlyLoanPayment	ListingCategory	BorrowerAPR	BorrowerRate	CreditScoreRangeLower	CreditScoreRangeUpper	IncomeRange	IncomeVerifiable	StatedMonthlyIncome	DebtToIncomeRatio	EmploymentStatus	EmploymentStatusDuration	IsBorrowerHomeowner	CreditRiskScore
2	0EE9337825851032864889A	2007-01-05 15:00:47.090	2009-12-17	Completed	36	3001	123.32	0	0.28269	0.2750	480.0	499.0	Not displayed	True	2083.333333	0.06	Not available	NaN	False	HR
17	0FDF336575182398852A8DC	2006-08-15 12:21:09.433	2008-05-22	Chargedoff	36	10000	334.54	0	0.13202	0.1250	760.0	779.0	Not displayed	True	5833.333333	0.12	Not available	NaN	True	AA
42	10AD33803102786533722BB	2007-02-04 17:24:27.850	2008-08-08	Chargedoff	36	1500	62.86	0	0.30300	0.2900	520.0	539.0	Not displayed	True	416.666667	0.27	Not available	NaN	False	HR
63	0F1A336989050248528ED31	2006-10-07 00:32:48.020	2007-04-13	Completed	36	6000	251.43	0	0.29776	0.2900	620.0	639.0	Not displayed	True	7500.000000	0.25	Not available	NaN	True	D
76	0DFD336490760860963B515	2006-07-11 20:48:47.660	2007-08-10	Completed	36	2200	87.47	0	0.25861	0.2500	540.0	559.0	Not displayed	True	8583.333333	0.13	Not available	NaN	True	E
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
113797	E467337109094381251D0BB	2006-10-18 11:35:37.843	2008-03-30	Defaulted	36	4500	147.32	0	0.11696	0.1100	660.0	679.0	Not displayed	True	4166.666667	0.04	Not available	NaN	False	C
113819	CF913368831369031FEEAB6	2006-09-14 13:19:21.320	2009-09-03	Completed	36	3000	107.56	0	0.18123	0.1740	660.0	679.0	Not displayed	True	4583.333333	0.13	Not available	NaN	True	C
113825	CFB1338127898040944BD47	2007-02-05 11:42:34.790	2010-01-13	Completed	36	2550	106.86	0	0.29776	0.2900	520.0	539.0	Not displayed	True	8833.333333	0.08	Not available	NaN	False	HR
113848	FFF0338012502972731B2C1	2007-02-07 15:04:40.563	2010-02-13	Completed	36	1000	0.00	0	0.30709	0.2875	0.0	19.0	Not displayed	True	833.333333	NaN	Not available	NaN	False	NC
113926	E34333654295683286C35C2	2006-08-01 10:31:31.143	2007-09-06	Defaulted	36	3000	125.72	0	0.29776	0.2900	540.0	559.0	Not displayed	True	5416.666667	0.07	Not available	NaN	False	E

Part I - Prosper Loan Dataset¶

by Festus Kipsang Sambu¶

Introduction¶

checking for Duplicate listings¶

Preminary EDA using pandas Profiling¶

Data cleaning¶

What is the structure of your dataset?¶

What is/are the main feature(s) of interest in your dataset?¶

What features in the dataset do you think will help support your investigation into your feature(s) of interest?¶

Univariate Exploration¶

Listing Category¶

MonthlyLoanPayment¶

CreditScoreRangeLower and CreditScoreRangeUpper¶

ListingCreationDate and ClosedDate¶

Discuss the distribution(s) of your variable(s) of interest. Were there any unusual points? Did you need to perform any transformation¶

Of the features you investigated, were there any unusual distributions? Did you perform any operations on the data to tidy, adjust, or change the form of the data? If so, why did you do this?¶

Bivariate Exploration¶

Categorical variables¶

Boolean Variables¶

Talk about some of the relationships you observed in this part of the investigation. How did the feature(s) of interest vary with other features in the dataset?¶

Did you observe any interesting relationships between the other features (not the main feature(s) of interest)?¶

Multivariate Exploration¶

EmploymentStatus¶

Talk about some of the relationships you observed in this part of the investigation. Were there features that strengthened each other in terms of looking at your feature(s) of interest?¶

Were there any interesting or surprising interactions between features?¶

	ListingKey	ListingNumber	ListingCreationDate	CreditGrade	Term	LoanStatus	ClosedDate	BorrowerAPR	BorrowerRate	LenderYield	EstimatedEffectiveYield	EstimatedLoss	EstimatedReturn	ProsperRating (numeric)	ProsperRating (Alpha)	ProsperScore	ListingCategory (numeric)	BorrowerState	Occupation	EmploymentStatus	EmploymentStatusDuration	IsBorrowerHomeowner	CurrentlyInGroup	GroupKey	DateCreditPulled	CreditScoreRangeLower	CreditScoreRangeUpper	FirstRecordedCreditLine	CurrentCreditLines	OpenCreditLines	TotalCreditLinespast7years	OpenRevolvingAccounts	OpenRevolvingMonthlyPayment	InquiriesLast6Months	TotalInquiries	CurrentDelinquencies	AmountDelinquent	DelinquenciesLast7Years	PublicRecordsLast10Years	PublicRecordsLast12Months	RevolvingCreditBalance	BankcardUtilization	AvailableBankcardCredit	TotalTrades	TradesNeverDelinquent (percentage)	TradesOpenedLast6Months	DebtToIncomeRatio	IncomeRange	IncomeVerifiable	StatedMonthlyIncome	LoanKey	TotalProsperLoans	TotalProsperPaymentsBilled	OnTimeProsperPayments	ProsperPaymentsLessThanOneMonthLate	ProsperPaymentsOneMonthPlusLate	ProsperPrincipalBorrowed	ProsperPrincipalOutstanding	ScorexChangeAtTimeOfListing	LoanCurrentDaysDelinquent	LoanFirstDefaultedCycleNumber	LoanMonthsSinceOrigination	LoanNumber	LoanOriginalAmount	LoanOriginationDate	LoanOriginationQuarter	MemberKey	MonthlyLoanPayment	LP_CustomerPayments	LP_CustomerPrincipalPayments	LP_InterestandFees	LP_ServiceFees	LP_CollectionFees	LP_GrossPrincipalLoss	LP_NetPrincipalLoss	LP_NonPrincipalRecoverypayments	PercentFunded	Recommendations	InvestmentFromFriendsCount	InvestmentFromFriendsAmount	Investors
count	113937	1.139370e+05	113937	28953	113937.000000	113937	55089	113912.000000	113937.000000	113937.000000	84853.000000	84853.000000	84853.000000	84853.000000	84853	84853.000000	113937.000000	108422	110349	111682	106312.000000	113937	113937	13341	113937	113346.000000	113346.000000	113240	106333.000000	106333.000000	113240.000000	113937.00000	113937.000000	113240.000000	112778.000000	113240.000000	106315.000000	112947.000000	113240.000000	106333.000000	1.063330e+05	106333.000000	106393.000000	106393.000000	106393.000000	106393.000000	105383.000000	113937	113937	1.139370e+05	113937	22085.0000	22085.000000	22085.000000	22085.000000	22085.00000	22085.000000	22085.000000	18928.000000	113937.000000	16952.000000	113937.000000	113937.000000	113937.00000	113937	113937	113937	113937.000000	113937.000000	113937.000000	113937.000000	113937.000000	113937.000000	113937.000000	113937.000000	113937.000000	113937.000000	113937.000000	113937.00000	113937.000000	113937.000000
unique	113066	NaN	113064	8	NaN	12	2802	NaN	NaN	NaN	NaN	NaN	NaN	NaN	7	NaN	NaN	51	67	8	NaN	2	2	706	112992	NaN	NaN	11585	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	8	2	NaN	113066	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1873	33	90831	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
top	17A93590655669644DB4C06	NaN	2013-10-02 17:20:16.550000000	C	NaN	Current	2014-03-04 00:00:00	NaN	NaN	NaN	NaN	NaN	NaN	NaN	C	NaN	NaN	CA	Other	Employed	NaN	True	False	783C3371218786870A73D20	2013-12-23 09:38:12	NaN	NaN	1993-12-01 00:00:00	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	$25,000-49,999	True	NaN	CB1B37030986463208432A1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2014-01-22 00:00:00	Q4 2013	63CA34120866140639431C9	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
freq	6	NaN	6	5649	NaN	56576	105	NaN	NaN	NaN	NaN	NaN	NaN	NaN	18345	NaN	NaN	14717	28617	67322	NaN	57478	101218	1140	6	NaN	NaN	185	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	32192	105268	NaN	6	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	491	14450	9	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
mean	NaN	6.278857e+05	NaN	NaN	40.830248	NaN	NaN	0.218828	0.192764	0.182701	0.168661	0.080306	0.096068	4.072243	NaN	5.950067	2.774209	NaN	NaN	NaN	96.071582	NaN	NaN	NaN	NaN	685.567731	704.567731	NaN	10.317192	9.260164	26.754539	6.96979	398.292161	1.435085	5.584405	0.592052	984.507059	4.154984	0.312646	0.015094	1.759871e+04	0.561309	11210.225447	23.230034	0.885897	0.802327	0.275947	NaN	NaN	5.608026e+03	NaN	1.4211	22.934345	22.271949	0.613629	0.04854	8472.311961	2930.313906	-3.223214	152.816539	16.268464	31.896882	69444.474271	8337.01385	NaN	NaN	NaN	272.475783	4183.079489	3105.536588	1077.542901	-54.725641	-14.242698	700.446342	681.420499	25.142686	0.998584	0.048027	0.02346	16.550751	80.475228
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
min	NaN	4.000000e+00	NaN	NaN	12.000000	NaN	NaN	0.006530	0.000000	-0.010000	-0.182700	0.004900	-0.182700	1.000000	NaN	1.000000	0.000000	NaN	NaN	NaN	0.000000	NaN	NaN	NaN	NaN	0.000000	19.000000	NaN	0.000000	0.000000	2.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	NaN	NaN	0.000000e+00	NaN	0.0000	0.000000	0.000000	0.000000	0.00000	0.000000	0.000000	-209.000000	0.000000	0.000000	0.000000	1.000000	1000.00000	NaN	NaN	NaN	0.000000	-2.349900	0.000000	-2.349900	-664.870000	-9274.750000	-94.200000	-954.550000	0.000000	0.700000	0.000000	0.00000	0.000000	1.000000
25%	NaN	4.009190e+05	NaN	NaN	36.000000	NaN	NaN	0.156290	0.134000	0.124200	0.115670	0.042400	0.074080	3.000000	NaN	4.000000	1.000000	NaN	NaN	NaN	26.000000	NaN	NaN	NaN	NaN	660.000000	679.000000	NaN	7.000000	6.000000	17.000000	4.00000	114.000000	0.000000	2.000000	0.000000	0.000000	0.000000	0.000000	0.000000	3.121000e+03	0.310000	880.000000	15.000000	0.820000	0.000000	0.140000	NaN	NaN	3.200333e+03	NaN	1.0000	9.000000	9.000000	0.000000	0.00000	3500.000000	0.000000	-35.000000	0.000000	9.000000	6.000000	37332.000000	4000.00000	NaN	NaN	NaN	131.620000	1005.760000	500.890000	274.870000	-73.180000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.00000	0.000000	2.000000
50%	NaN	6.005540e+05	NaN	NaN	36.000000	NaN	NaN	0.209760	0.184000	0.173000	0.161500	0.072400	0.091700	4.000000	NaN	6.000000	1.000000	NaN	NaN	NaN	67.000000	NaN	NaN	NaN	NaN	680.000000	699.000000	NaN	10.000000	9.000000	25.000000	6.00000	271.000000	1.000000	4.000000	0.000000	0.000000	0.000000	0.000000	0.000000	8.549000e+03	0.600000	4100.000000	22.000000	0.940000	0.000000	0.220000	NaN	NaN	4.666667e+03	NaN	1.0000	16.000000	15.000000	0.000000	0.00000	6000.000000	1626.550000	-3.000000	0.000000	14.000000	21.000000	68599.000000	6500.00000	NaN	NaN	NaN	217.740000	2583.830000	1587.500000	700.840100	-34.440000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.00000	0.000000	44.000000
75%	NaN	8.926340e+05	NaN	NaN	36.000000	NaN	NaN	0.283810	0.250000	0.240000	0.224300	0.112000	0.116600	5.000000	NaN	8.000000	3.000000	NaN	NaN	NaN	137.000000	NaN	NaN	NaN	NaN	720.000000	739.000000	NaN	13.000000	12.000000	35.000000	9.00000	525.000000	2.000000	7.000000	0.000000	0.000000	3.000000	0.000000	0.000000	1.952100e+04	0.840000	13180.000000	30.000000	1.000000	1.000000	0.320000	NaN	NaN	6.825000e+03	NaN	2.0000	33.000000	32.000000	0.000000	0.00000	11000.000000	4126.720000	25.000000	0.000000	22.000000	65.000000	101901.000000	12000.00000	NaN	NaN	NaN	371.580000	5548.400000	4000.000000	1458.540000	-13.920000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.00000	0.000000	115.000000
max	NaN	1.255725e+06	NaN	NaN	60.000000	NaN	NaN	0.512290	0.497500	0.492500	0.319900	0.366000	0.283700	7.000000	NaN	11.000000	20.000000	NaN	NaN	NaN	755.000000	NaN	NaN	NaN	NaN	880.000000	899.000000	NaN	59.000000	54.000000	136.000000	51.00000	14985.000000	105.000000	379.000000	83.000000	463881.000000	99.000000	38.000000	20.000000	1.435667e+06	5.950000	646285.000000	126.000000	1.000000	20.000000	10.010000	NaN	NaN	1.750003e+06	NaN	8.0000	141.000000	141.000000	42.000000	21.00000	72499.000000	23450.950000	286.000000	2704.000000	44.000000	100.000000	136486.000000	35000.00000	NaN	NaN	NaN	2251.510000	40702.390000	35000.000000	15617.030000	32.060000	0.000000	25000.000000	25000.000000	21117.900000	1.012500	39.000000	33.00000	25000.000000	1189.000000

Variable	Description
ListingKey	Unique key for each listing, same value as the 'key' used in the listing object in the API.
ListingNumber	The number that uniquely identifies the listing to the public as displayed on the website.
ListingCreationDate	The date the listing was created.
CreditGrade	The Credit rating that was assigned at the time the listing went live. Applicable for listings pre-2009 period and will only be populated for those listings.
Term	The length of the loan expressed in months.
LoanStatus	The current status of the loan: Cancelled, Chargedoff, Completed, Current, Defaulted, FinalPaymentInProgress, PastDue. The PastDue status will be accompanied by a delinquency bucket.
ClosedDate	Closed date is applicable for Cancelled, Completed, Chargedoff and Defaulted loan statuses.
BorrowerAPR	The Borrower's Annual Percentage Rate (APR) for the loan.
BorrowerRate	The Borrower's interest rate for this loan.
LenderYield	The Lender yield on the loan. Lender yield is equal to the interest rate on the loan less the servicing fee.
EstimatedEffectiveYield	Effective yield is equal to the borrower interest rate (i) minus the servicing fee rate, (ii) minus estimated uncollected interest on charge-offs, (iii) plus estimated collected late fees. Applicable for loans originated after July 2009.
EstimatedLoss	Estimated loss is the estimated principal loss on charge-offs. Applicable for loans originated after July 2009.
EstimatedReturn	The estimated return assigned to the listing at the time it was created. Estimated return is the difference between the Estimated Effective Yield and the Estimated Loss Rate. Applicable for loans originated after July 2009.
ProsperRating (numeric)	The Prosper Rating assigned at the time the listing was created: 0 - N/A, 1 - HR, 2 - E, 3 - D, 4 - C, 5 - B, 6 - A, 7 - AA. Applicable for loans originated after July 2009.
ProsperRating (Alpha)	The Prosper Rating assigned at the time the listing was created between AA - HR. Applicable for loans originated after July 2009.
ProsperScore	A custom risk score built using historical Prosper data. The score ranges from 1-10, with 10 being the best, or lowest risk score. Applicable for loans originated after July 2009.
ListingCategory	The category of the listing that the borrower selected when posting their listing: 0 - Not Available, 1 - Debt Consolidation, 2 - Home Improvement, 3 - Business, 4 - Personal Loan, 5 - Student Use, 6 - Auto, 7- Other, 8 - Baby&Adoption, 9 - Boat, 10 - Cosmetic Procedure, 11 - Engagement Ring, 12 - Green Loans, 13 - Household Expenses, 14 - Large Purchases, 15 - Medical/Dental, 16 - Motorcycle, 17 - RV, 18 - Taxes, 19 - Vacation, 20 - Wedding Loans
BorrowerState	The two letter abbreviation of the state of the address of the borrower at the time the Listing was created.
Occupation	The Occupation selected by the Borrower at the time they created the listing.
EmploymentStatus	The employment status of the borrower at the time they posted the listing.
EmploymentStatusDuration	The length in months of the employment status at the time the listing was created.
IsBorrowerHomeowner	A Borrower will be classified as a homowner if they have a mortgage on their credit profile or provide documentation confirming they are a homeowner.
CurrentlyInGroup	Specifies whether or not the Borrower was in a group at the time the listing was created.
GroupKey	The Key of the group in which the Borrower is a member of. Value will be null if the borrower does not have a group affiliation.
DateCreditPulled	The date the credit profile was pulled.
CreditScoreRangeLower	The lower value representing the range of the borrower's credit score as provided by a consumer credit rating agency.
CreditScoreRangeUpper	The upper value representing the range of the borrower's credit score as provided by a consumer credit rating agency.
FirstRecordedCreditLine	The date the first credit line was opened.
CurrentCreditLines	Number of current credit lines at the time the credit profile was pulled.
OpenCreditLines	Number of open credit lines at the time the credit profile was pulled.
TotalCreditLinespast7years	Number of credit lines in the past seven years at the time the credit profile was pulled.
OpenRevolvingAccounts	Number of open revolving accounts at the time the credit profile was pulled.
OpenRevolvingMonthlyPayment	Monthly payment on revolving accounts at the time the credit profile was pulled.
InquiriesLast6Months	Number of inquiries in the past six months at the time the credit profile was pulled.
TotalInquiries	Total number of inquiries at the time the credit profile was pulled.
CurrentDelinquencies	Number of accounts delinquent at the time the credit profile was pulled.
AmountDelinquent	Dollars delinquent at the time the credit profile was pulled.
DelinquenciesLast7Years	Number of delinquencies in the past 7 years at the time the credit profile was pulled.
PublicRecordsLast10Years	Number of public records in the past 10 years at the time the credit profile was pulled.
PublicRecordsLast12Months	Number of public records in the past 12 months at the time the credit profile was pulled.
RevolvingCreditBalance	Dollars of revolving credit at the time the credit profile was pulled.
BankcardUtilization	The percentage of available revolving credit that is utilized at the time the credit profile was pulled.
AvailableBankcardCredit	The total available credit via bank card at the time the credit profile was pulled.
TotalTrades	Number of trade lines ever opened at the time the credit profile was pulled.
TradesNeverDelinquent	Number of trades that have never been delinquent at the time the credit profile was pulled.
TradesOpenedLast6Months	Number of trades opened in the last 6 months at the time the credit profile was pulled.
DebtToIncomeRatio	The debt to income ratio of the borrower at the time the credit profile was pulled. This value is Null if the debt to income ratio is not available. This value is capped at 10.01 (any debt to income ratio larger than 1000% will be returned as 1001%).
IncomeRange	The income range of the borrower at the time the listing was created.
IncomeVerifiable	The borrower indicated they have the required documentation to support their income.
StatedMonthlyIncome	The monthly income the borrower stated at the time the listing was created.
LoanKey	Unique key for each loan. This is the same key that is used in the API.
TotalProsperLoans	Number of Prosper loans the borrower at the time they created this listing. This value will be null if the borrower had no prior loans.
TotalProsperPaymentsBilled	Number of on time payments the borrower made on Prosper loans at the time they created this listing. This value will be null if the borrower had no prior loans.
OnTimeProsperPayments	Number of on time payments the borrower had made on Prosper loans at the time they created this listing. This value will be null if the borrower has no prior loans.
ProsperPaymentsLessThanOneMonthLate	Number of payments the borrower made on Prosper loans that were less than one month late at the time they created this listing. This value will be null if the borrower had no prior loans.
ProsperPaymentsOneMonthPlusLate	Number of payments the borrower made on Prosper loans that were greater than one month late at the time they created this listing. This value will be null if the borrower had no prior loans.
ProsperPrincipalBorrowed	Total principal borrowed on Prosper loans at the time the listing was created. This value will be null if the borrower had no prior loans.
ProsperPrincipalOutstanding	Principal outstanding on Prosper loans at the time the listing was created. This value will be null if the borrower had no prior loans.
ScorexChangeAtTimeOfListing	Borrower's credit score change at the time the credit profile was pulled. This will be the change relative to the borrower's last Prosper loan. This value will be null if the borrower had no prior loans.
LoanCurrentDaysDelinquent	The number of days delinquent.
LoanFirstDefaultedCycleNumber	The cycle the loan was charged off. If the loan has not charged off the value will be null.
LoanMonthsSinceOrigination	Number of months since the loan originated.
LoanNumber	Unique numeric value associated with the loan.
LoanOriginalAmount	The origination amount of the loan.
LoanOriginationDate	The date the loan was originated.
LoanOriginationQuarter	The quarter in which the loan was originated.
MemberKey	The unique key that is associated with the borrower. This is the same identifier that is used in the API member object.
MonthlyLoanPayment	The scheduled monthly loan payment.
LP_CustomerPayments	Pre charge-off cumulative gross payments made by the borrower on the loan. If the loan has charged off, this value will exclude any recoveries.
LP_CustomerPrincipalPayments	Pre charge-off cumulative principal payments made by the borrower on the loan. If the loan has charged off, this value will exclude any recoveries.
LP_InterestandFees	Pre charge-off cumulative interest and fees paid by the borrower. If the loan has charged off, this value will exclude any recoveries.
LP_ServiceFees	Cumulative service fees paid by the investors who have invested in the loan.
LP_CollectionFees	Cumulative collection fees paid by the investors who have invested in the loan.
LP_GrossPrincipalLoss	The gross charged off amount of the loan.
LP_NetPrincipalLoss	The principal that remains uncollected after any recoveries.
LP_NonPrincipalRecoverypayments	The interest and fee component of any recovery payments. The current payment policy applies payments in the following order: Fees, interest, principal.
PercentFunded	Percent the listing was funded.
Recommendations	Number of recommendations the borrower had at the time the listing was created.
InvestmentFromFriendsCount	Number of friends that made an investment in the loan.
InvestmentFromFriendsAmount	Dollar amount of investments that were made by friends.
Investors	The number of investors that funded the loan.

	ListingKey	ListingNumber	ListingCreationDate	CreditGrade	Term	LoanStatus	ClosedDate	BorrowerAPR	BorrowerRate	LenderYield	EstimatedEffectiveYield	EstimatedLoss	EstimatedReturn	ProsperRating (numeric)	ProsperRating (Alpha)	ProsperScore	ListingCategory (numeric)	BorrowerState	Occupation	EmploymentStatus	EmploymentStatusDuration	IsBorrowerHomeowner	CurrentlyInGroup	GroupKey	DateCreditPulled	CreditScoreRangeLower	CreditScoreRangeUpper	FirstRecordedCreditLine	CurrentCreditLines	OpenCreditLines	TotalCreditLinespast7years	OpenRevolvingAccounts	OpenRevolvingMonthlyPayment	InquiriesLast6Months	TotalInquiries	RevolvingCreditBalance	BankcardUtilization	AvailableBankcardCredit	TotalTrades	TradesNeverDelinquent (percentage)	TradesOpenedLast6Months	DebtToIncomeRatio	IncomeRange	IncomeVerifiable	StatedMonthlyIncome	LoanKey	TotalProsperLoans	TotalProsperPaymentsBilled	OnTimeProsperPayments	ProsperPaymentsLessThanOneMonthLate	ProsperPaymentsOneMonthPlusLate	ProsperPrincipalBorrowed	ProsperPrincipalOutstanding	ScorexChangeAtTimeOfListing	LoanFirstDefaultedCycleNumber	LoanMonthsSinceOrigination	LoanNumber	LoanOriginalAmount	LoanOriginationDate	LoanOriginationQuarter	MemberKey	MonthlyLoanPayment	PercentFunded	Investors
998	0A0635972629771021E38F3	1055932	2013-12-18 14:25:17.053000000	NaN	60	Current	NaN	0.1771	0.1535	0.1435	0.13507	0.0599	0.07517	5.0	B	6.0	1	MD	Other	Employed	100.0	True	False	NaN	2014-02-08 11:39:13	760.0	779.0	1994-11-01 00:00:00	16.0	16.0	34.0	15	623.0	1.0	5.0	8028.0	0.27	14190.0	32.0	0.96	1.0	0.35	$25,000-49,999	True	3750.0	CB643706582969191E8C26B	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1	132043	10000	2014-02-12 00:00:00	Q1 2014	98A937006186534818378B9	239.74	1.0	1
999	0A0635972629771021E38F3	1055932	2013-12-18 14:25:17.053000000	NaN	60	Current	NaN	0.1771	0.1535	0.1435	0.13507	0.0599	0.07517	5.0	B	7.0	1	MD	Other	Employed	100.0	True	False	NaN	2014-02-08 11:39:13	760.0	779.0	1994-11-01 00:00:00	16.0	16.0	34.0	15	623.0	1.0	5.0	8028.0	0.27	14190.0	32.0	0.96	1.0	0.35	$25,000-49,999	True	3750.0	CB643706582969191E8C26B	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1	132043	10000	2014-02-12 00:00:00	Q1 2014	98A937006186534818378B9	239.74	1.0	1
57494	0A0635972629771021E38F3	1055932	2013-12-18 14:25:17.053000000	NaN	60	Current	NaN	0.1771	0.1535	0.1435	0.13507	0.0599	0.07517	5.0	B	8.0	1	MD	Other	Employed	100.0	True	False	NaN	2014-02-08 11:39:13	760.0	779.0	1994-11-01 00:00:00	16.0	16.0	34.0	15	623.0	1.0	5.0	8028.0	0.27	14190.0	32.0	0.96	1.0	0.35	$25,000-49,999	True	3750.0	CB643706582969191E8C26B	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1	132043	10000	2014-02-12 00:00:00	Q1 2014	98A937006186534818378B9	239.74	1.0	1

	ListingKey	ListingNumber	ListingCreationDate	CreditGrade	Term	LoanStatus	ClosedDate	BorrowerAPR	BorrowerRate	LenderYield	EstimatedEffectiveYield	EstimatedLoss	EstimatedReturn	ProsperRating (numeric)	ProsperRating (Alpha)	ProsperScore	ListingCategory (numeric)	BorrowerState	Occupation	EmploymentStatus	EmploymentStatusDuration	IsBorrowerHomeowner	CurrentlyInGroup	GroupKey	DateCreditPulled	CreditScoreRangeLower	CreditScoreRangeUpper	FirstRecordedCreditLine	CurrentCreditLines	OpenCreditLines	TotalCreditLinespast7years	OpenRevolvingAccounts	OpenRevolvingMonthlyPayment	InquiriesLast6Months	TotalInquiries	CurrentDelinquencies	AmountDelinquent	DelinquenciesLast7Years	PublicRecordsLast10Years	PublicRecordsLast12Months	RevolvingCreditBalance	BankcardUtilization	AvailableBankcardCredit	TotalTrades	TradesNeverDelinquent (percentage)	TradesOpenedLast6Months	DebtToIncomeRatio	IncomeRange	IncomeVerifiable	StatedMonthlyIncome	LoanKey	TotalProsperLoans	TotalProsperPaymentsBilled	OnTimeProsperPayments	ProsperPaymentsLessThanOneMonthLate	ProsperPaymentsOneMonthPlusLate	ProsperPrincipalBorrowed	ProsperPrincipalOutstanding	ScorexChangeAtTimeOfListing	LoanCurrentDaysDelinquent	LoanFirstDefaultedCycleNumber	LoanMonthsSinceOrigination	LoanNumber	LoanOriginalAmount	LoanOriginationDate	LoanOriginationQuarter	MemberKey	MonthlyLoanPayment	LP_CustomerPayments	LP_CustomerPrincipalPayments	LP_InterestandFees	LP_ServiceFees	LP_CollectionFees	LP_GrossPrincipalLoss	LP_NetPrincipalLoss	LP_NonPrincipalRecoverypayments	PercentFunded	Recommendations	InvestmentFromFriendsCount	InvestmentFromFriendsAmount	Investors
1554	2D2635984503681189056B4	1097584	2014-01-11 07:50:08.037000000	NaN	60	Current	NaN	0.14528	0.1224	0.1124	0.10731	0.0424	0.06491	5.0	B	9.0	1	NJ	Police Officer/Correction Officer	Employed	107.0	True	False	NaN	2014-03-07 10:04:00	720.0	739.0	1996-07-01 00:00:00	19.0	18.0	27.0	15	1024.0	1.0	5.0	0.0	0.0	0.0	0.0	0.0	20802.0	0.4	26503.0	26.0	1.0	1.0	0.18	$100,000+	True	10833.333333	DC303708663304668E74587	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0	NaN	0	135375	35000	2014-03-11 00:00:00	Q1 2014	07B33702526608187DB957A	782.81	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0	0	0.0	1
2539	2D2635984503681189056B4	1097584	2014-01-11 07:50:08.037000000	NaN	60	Current	NaN	0.14528	0.1224	0.1124	0.10731	0.0424	0.06491	5.0	B	10.0	1	NJ	Police Officer/Correction Officer	Employed	107.0	True	False	NaN	2014-03-07 10:04:00	720.0	739.0	1996-07-01 00:00:00	19.0	18.0	27.0	15	1024.0	1.0	5.0	0.0	0.0	0.0	0.0	0.0	20802.0	0.4	26503.0	26.0	1.0	1.0	0.18	$100,000+	True	10833.333333	DC303708663304668E74587	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0	NaN	0	135375	35000	2014-03-11 00:00:00	Q1 2014	07B33702526608187DB957A	782.81	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0	0	0.0	1

	ListingKey	ListingCreationDate	ClosedDate	LoanStatus	Term	LoanOriginalAmount	MonthlyLoanPayment	ListingCategory	BorrowerAPR	BorrowerRate	CreditGrade	ProsperRating	CreditScoreRangeLower	CreditScoreRangeUpper	IncomeRange	IncomeVerifiable	StatedMonthlyIncome	DebtToIncomeRatio	EmploymentStatus	EmploymentStatusDuration	IsBorrowerHomeowner
113921	D21A33647075665665BE266	2005-11-09 20:44:28.847	2005-11-25	Completed	36	1000	29.52	0	NaN	0.0400	AA	NaN	NaN	NaN	Not displayed	True	12500.000000	0.23284	NaN	NaN	False
106735	97B333660731451030726AF	2005-11-11 08:46:17.303	2006-02-07	Completed	36	1500	45.60	0	NaN	0.0595	AA	NaN	NaN	NaN	Not displayed	True	14583.333333	0.01747	NaN	NaN	False
25166	B3083364738592238DBDA63	2005-11-15 16:06:47.457	2005-11-29	Completed	36	1000	29.68	0	NaN	0.0435	AA	NaN	NaN	NaN	Not displayed	True	9166.666667	0.04120	NaN	NaN	False
62297	2F25336514614362295DA03	2005-11-15 18:25:56.107	2005-11-30	Completed	36	1000	35.65	0	NaN	0.1700	E	NaN	NaN	NaN	Not displayed	True	10000.000000	0.01783	NaN	NaN	False
90598	B87F3364623805558A45EAB	2005-11-18 13:39:03.263	2005-11-29	Completed	36	1000	33.21	0	NaN	0.1200	B	NaN	NaN	NaN	Not displayed	True	16666.666667	0.01051	NaN	NaN	False
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
57106	21F83380195431266D92121	2007-02-05 20:54:50.233	2007-12-10	Completed	36	5000	170.89	0	0.14709	0.1400	D	NaN	620.0	639.0	Not displayed	True	5416.666667	0.07000	NaN	NaN	False
13239	B1A13379859311513B13011	2007-02-07 11:53:37.327	2010-01-20	Completed	36	2000	61.75	0	0.07852	0.0700	B	NaN	700.0	719.0	Not displayed	True	2333.333333	0.26000	NaN	NaN	False
22979	57AB338020191550932BC4A	2007-02-07 12:03:49.340	2010-02-02	Completed	36	2000	64.42	0	0.15239	0.1435	D	NaN	600.0	619.0	Not displayed	True	1916.666667	0.04000	NaN	NaN	False
66469	6F793380667470304C450E0	2007-02-07 21:07:47.410	2007-09-04	Completed	36	4000	140.41	0	0.16607	0.1589	D	NaN	600.0	619.0	Not displayed	True	2500.000000	0.09000	NaN	NaN	False
7312	00B1338100511182555E371	2007-02-09 08:00:09.510	2007-05-16	Completed	36	5000	159.81	0	0.10039	0.0935	A	NaN	740.0	759.0	Not displayed	True	5250.000000	0.08000	NaN	NaN	True

	CreditScoreRangeLower	CreditScoreRangeUpper
count	110809.000000	110809.000000
mean	686.846917	705.846917
std	64.549893	64.549893
min	0.000000	19.000000
25%	660.000000	679.000000
50%	680.000000	699.000000
75%	720.000000	739.000000
max	880.000000	899.000000

	LoanStatus	Term	LoanOriginalAmount	MonthlyLoanPayment	ListingCategory	BorrowerAPR	CreditScoreRangeLower	IncomeRange	IncomeVerifiable	StatedMonthlyIncome	DebtToIncomeRatio	EmploymentStatus	EmploymentStatusDuration	IsBorrowerHomeowner	CreditRiskScore	MonthsToClosed
84581	Current	36	20000	703.04	1	0.19645	740.0	$25,000-49,999	True	2916.666667	0.78	Employed	414.0	False	B	NaN
75489	Current	60	10000	253.66	1	0.20347	700.0	$50,000-74,999	True	5884.083333	0.36	Employed	388.0	True	C	NaN
112937	Completed	36	1200	45.44	1	0.23635	580.0	$25,000-49,999	True	4041.666667	0.14	Full-time	2.0	True	E	10.817918
97928	Current	36	7550	249.08	1	0.14348	760.0	$50,000-74,999	True	5666.666667	0.10	Employed	2.0	True	A	NaN
15126	Defaulted	36	5000	173.33	0	0.15713	640.0	$25,000-49,999	True	3880.000000	0.07	Full-time	69.0	False	C	28.391151
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
36372	Current	60	9000	232.23	1	0.21192	660.0	$50,000-74,999	True	4583.333333	0.08	Employed	1.0	False	C	NaN
22562	Completed	36	8500	275.63	8	0.13138	760.0	$75,000-99,999	True	6250.000000	0.24	Employed	147.0	True	A	8.628991
108827	Current	36	4000	135.84	1	0.17151	680.0	$25,000-49,999	True	2500.000000	0.22	Employed	39.0	False	B	NaN
102049	Current	36	10000	400.35	1	0.29394	680.0	$50,000-74,999	False	5000.000000	NaN	Employed	121.0	False	D	NaN
56149	FinalPaymentInProgress	36	3000	103.54	1	0.18316	720.0	$50,000-74,999	True	4166.666667	0.20	Full-time	94.0	True	B	NaN